Parametric Clutter Rejection for Space-time Adaptive Processing

Practical STAP implementations rely on reduced-dimension processing, using techniques such as principle components or partially adaptive filters. The dimension reduction not only decreases the computational load, it also reduces the sample support required for estimating the interference statistics. This results because the clutter covariance is implicitly assumed to possess a certain (nonparametric) structure. In this paper, we demonstrate how imposing a parametric structure on the clutter and jamming can lead to a further reduction in both computation and secondary sample support. Our approach, referred to as Space-Time AutoRegressive (STAR) filtering, is applied in two steps: First, a structured subspace orthogonal to that in which the clutter and interference reside is found; Second, a detector matched to this subspace is used to determine whether or not a target is present. Using a realistic simulated data set for circular array STAP, we demonstrate that this approach achieves significantly lower SINR loss with a computational load that is less than that required by the reduced-dimension PRI-staggered STAP method. The STAR algorithm also yields excellent performance with very small secondary sample support, a feature that is particularly attractive for circular array STAP where the clutter statistics are range dependent.